• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Sparse Target Matrix Generation Based Unsupervised Feature Learning Algorithm for Image Classification

Vol. 12, No.6, June 30, 2018
10.3837/tiis.2018.06.020, Download Paper (Free):

Abstract

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm (L2) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.


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Cite this article

[IEEE Style]
Dan Zhao, Baolong Guo and Yunyi Yan, "A Sparse Target Matrix Generation Based Unsupervised Feature Learning Algorithm for Image Classification," KSII Transactions on Internet and Information Systems, vol. 12, no. 6, pp. 2806-2825, 2018. DOI: 10.3837/tiis.2018.06.020

[ACM Style]
Zhao, D., Guo, B., and Yan, Y. 2018. A Sparse Target Matrix Generation Based Unsupervised Feature Learning Algorithm for Image Classification. KSII Transactions on Internet and Information Systems, 12, 6, (2018), 2806-2825. DOI: 10.3837/tiis.2018.06.020